An Application Of Data Mining Technique On Intrusion Detection System

With the development at full speed of the network technology, the computer network is applied to each field of the human activity extensively, the impact on social economy and people's life of the network is greater and greater. The security question of the network receives the extensive concern more and more, technology and products that various kinds of network security are correlated with are emerging constantly. So it is urgent to establish a set of network security assurance system.It has been the focus of research. In nearly twenty years, researches on the Intrusion Detection, the core of Network Security, have been done at home and broad. Intrusion Detection Information Security, but it still has many problems, especially in self-completing and self-learning. To solve these problems, this thesis introduced elementary concept, principle and structure of intrusion detection and data mining technology, proposed a new model for the intrusion detection system that based on the data mining technology. We apply some existing algorithms of association analysis, sequence pattern analysis, and data classification to the Intrusion Detection System. Moreover, we draw characteristics and set up rules on the intrusion behaviors. We detect intrusion action by analyzing the audit data and patterns recognition to form an intelligible detection system. At last, misuse detection based connection(session) records and anomaly detection based user action are tested, simple model is implemented.